Target recognition of conventional Synthetic Aperture Radar (SAR) usually adopts the batch learning method.However, in practical application, the training data of the system cant be obtained all at one time.When new training sample arrives, the whole system needs to be retrained when using the method of batch learning.In order to solve this problem, ROSELM, an incremental learning algorithm, is applied to SAR target recognition, and Particle Swarm Optimization(PSO) algorithm is used to optimize the initial weight of ROSELM to improve its stability and recognition rate.The experimental results show that:1) When new SAR target samples are obtained, the system updating can be implemented simply by updating the output weights without re-training;2) The algorithm is very fast and has a high recognition rate, which is a good choice for online updating of SAR target recognition system.